Packetized Energy Management Control Systems and Methods of Using the Same

ABSTRACT

Aspects of the present disclosure include anonymous, asynchronous, and randomized control schemes for distributed energy resources (DERs). Such control schemes may include packetized energy management (PEM) control schemes for managing DERs that may provide near-optimal tracking performance under imperfect information and consumer quality of service (QoS) constraints.

RELATED APPLICATION DATA

This application is a continuation-in-part of U.S. patent applicationSer. No. 15/712,089, filed Sep. 21, 2017, and titled “Systems andMethods for Randomized, Packet-Based Power Management ofConditionally-Controlled Loads and Bi-Directional Distributed EnergyStorage Systems,” now pending, which application claims the benefit ofpriority of U.S. Provisional Patent Application Ser. No. 62/397,393,filed Sep. 21, 2016, and titled “Systems and Methods for Random-AccessPower Management of Thermostatically-Controlled Loads and Bi-DirectionalElectric Energy Systems.” This application also claims the benefit ofpriority of U.S. Provisional Patent Application Ser. No. 62/675,748,filed May 23, 2018, and titled “Systems And Methods For PacketizedEnergy Management: Asynchronous and Anonymous Coordination ofThermostatically Controlled Loads.” Each of these applications areincorporated by reference herein in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under contract nos.ECCS-1254549 and CNS-1735513 awarded by the National Science Foundation,and DE-AR0000694, awarded by the Department of Energy. The governmenthas certain rights in the invention.

FIELD OF THE INVENTION

The present disclosure relates to the management of distributed energyresources.

BACKGROUND

Fast-ramping generators have long provided reliable operating reservesfor power systems. However, power systems with high penetrations ofrenewable energy challenge this operating paradigm. At high levels ofrenewable penetration, current approaches to deal with the variabilityin wind or solar generation would require having more fast-rampingconventional generators online. However, that leads to more generatorsidling, burning fuel, and increasing harmful air-emissions, which alloppose the goals of a “green” energy future. Therefore, there is a needto move away from using such technologies to provide operating reserves,and to consider an active role for flexible and controllable net-loadenergy resources, e.g., plug-in electric vehicles (PEVs),thermostatically-controlled loads (TCLs), distributed energy storagesystems (DESSs), and distributed generation at the consumer level.

SUMMARY OF THE DISCLOSURE

In one implementation, the present disclosure is directed to a method ofproviding ancillary services to a power grid with a packetized energymanagement (PEM) coordinator. The method includes receiving a referencesignal from the power grid; filtering the reference signal to create afiltered reference signal; determining, according to the filteredreference signal, whether to grant or deny permission to a plurality ofdistributed energy resources (DERs) in communication with the PEMcoordinator to draw packets of power from or discharge packets of powerto the power grid; and granting or denying permission to the DERs todraw or discharge power packets.

In another implementation, the present disclosure is directed to amethod of providing ancillary services to a power grid with a packetizedenergy management (PEM) coordinator. The method includes determining,with a virtual battery model, an aggregate need for energy (NFE) of aplurality of distributed energy resources (DERs) in communication withthe coordinator; receiving, from a grid operator, a power grid referencesignal; and determining, from the NFE and the power grid referencesignal, whether to grant or deny requests from the DERs to receive ordischarge power packets.

In yet another implementation, the present disclosure is directed to acomputing device. The device includes a processor configured to performa packetized energy management (PEM) application, the PEM applicationincluding instructions for causing the processor to: receive a referencesignal from a power grid; filter the reference signal to create afiltered reference signal; determine, according to the filteredreference signal, whether to grant or deny permission to a plurality ofdistributed energy resources (DERs) in communication with the computingdevice to draw packets of power from or discharge packets of power tothe power grid; and grant or deny permission to the DERs to draw ordischarge power packets.

In yet another implementation, the present disclosure is directed to acomputing device. The computing device includes a processor configuredto perform a packetized energy management (PEM) application, the PEMapplication including instructions for causing the processor to:determine, with a virtual battery model, an aggregate need for energy(NFE) of a plurality of distributed energy resources (DERs) incommunication with the coordinator; receive, from a grid operator, apower grid reference signal; and determine, from the NFE and the powergrid reference signal, whether to grant or deny requests from the DERsto receive or discharge power packets.

In yet another implementation, the present disclosure is directed to amethod of providing ancillary services to a power grid with a packetizedenergy management (PEM) coordinator, the method includes communicatingwith a plurality of distributed energy resources (DERs) to grant or denypermission to the DERs to draw packets of power from, or dischargepackets of power to, the power grid; and randomizing a PEM controlparameter to minimize synchronization of the drawing or discharging ofpower packets by the DERs. In some examples, the coordinator determineswhether to grant or deny permission according to whether a power gridreference signal is greater than a grid status signal, wherein therandomizing includes randomizing granting permission when the power gridreference signal is greater than a grid status signal; wherein the PEMcontrol parameter is at least one of a control epoch length, acommunication epoch length, and a minimum off time between controlepochs, the method further comprising transmitting the randomized PEMcontrol parameter to the DERs; wherein the PEM control parameter is aPEM opt-in value for a locally-sensed condition of the DER for opting-into PEM control of the DER by the coordinator after the locally-sensedcondition exceeds a quality of service bound.

In yet another implementation, the present disclosure is directed to amethod of providing ancillary services to a power grid with a packetizedenergy management (PEM) coordinator. The method includes receiving apower grid reference signal; continuously determining, according to thereference signal, whether to grant or deny permission to a plurality ofdistributed energy resources (DERs) in communication with the PEMcoordinator to draw packets of power from or discharge packets of powerto the power grid; receiving a directional prediction of the power gridreference signal; and transmitting a PEM control signal to the pluralityof DERs according to the directional prediction. Some examples includewherein the PEM control signal is an instruction for a portion of theDERs to transition to a constant power consumption mode; and wherein thePEM control signal is an instruction for a portion of the DERs totransition to a higher or lower request probability profile, the requestprobability profile defining a probability a corresponding DER willrequest a power packet during a communication epoch.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 illustrates an example power system that incorporates packetizedenergy management coordinators for controlling a plurality ofdistributed energy resources (DERs);

FIG. 2 shows is a functional block diagram of one an exampleinstantiation of a coordinator and DER of FIG. 1;

FIG. 3 illustrates an example instantiation of the power system of FIG.1;

FIG. 4 illustrates an example of controlling a DER in the form of awater heater with an exemplary embodiment of a PEM controller;

FIG. 5 illustrates example probability profiles that may be used by aPEM controller;

FIGS. 6A-6C illustrate example PEM control schemes;

FIGS. 7A-7C illustrate results from an example simulation of a rapidincrease in power availability following a long shortage period with andwithout the use of a reference filter;

FIG. 8 shows charge and discharge probability profiles for a distributedenergy storage system (DESS);

FIG. 9 is a state flow diagram of an example DESS;

FIG. 10 shows an external variable load in a simulation;

FIG. 11 shows the supply with constant 60 percent base+40 percent DESSfor the simulation of FIG. 10;

FIG. 12 shows the state of charge (SOC) per 1000 agents over time forthe simulation of FIG. 10;

FIG. 13 shows fairness metrics (mean and standard deviation of SOC) forthe simulation of FIG. 10;

FIG. 14 shows transactions at each epoch for the simulation of FIG. 10;

FIG. 15 total buys, sells, and holds over time for the simulation ofFIG. 10;

FIG. 16 is a graph showing the independent and managed behavior of acoordinator over 8 hours (480 minutes) that consists of three differentload types: 1000 electric water heaters, 250 electric vehicle chargers,and 250 electric battery storage systems. The signal to be tracked bythe coordinator turns on at 160 minutes. Coordinator response is shownto track signal well as loads are being managed using the packetizedenergy management approach;

FIG. 17 shows two coordinators that are tracking a multi-mode referencesignal with different sets of DERs. The diverse coordinator (with 1000TCLs, 250 PEVs, and 250 ESS batteries) significantly outperforms the1000 TCL-only coordinators by leveraging the bidirectional capability ofthe batteries while maintaining QOS across all DER types. The TCL-onlycoordinators is unable to track due to large number of TCLs that enterexit-ON and opt out of PEM;

FIG. 18A shows the packetization effect for the diverse network of FIG.17;

FIG. 18B shows the packetization effect for the uniform network 1500electric water heaters of FIG. 17;

FIG. 19 illustrates a method of the present disclosure; and

FIG. 20 illustrates an example computer system that may be used toimplement one or more aspects of the present disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure include anonymous, asynchronous, andrandomized bottom-up control schemes for distributed energy resources(DERs). Such control schemes may include packetized energy management(PEM) control schemes for managing DERs that are configured to providenear-optimal tracking performance under imperfect information andconsumer quality of service (QoS) constraints.

FIG. 1 illustrates an example power system 100 that includes a pluralityof distributed energy resources (DERs) 102 that are aggregated to form avirtual power plant 104. In the illustrated example, each of DERs 102are configured to be connected to utility 106 through, e.g., power lines108 for exchange of power therebetween. In the illustrated example, DERs102 can be managed, or partially managed, using a virtual connection toa coordinator 110. DERs may be communicatively coupled to one or morecoordinators 110 by any means known in the art, e.g., local and/orremote communication utilizing any of a wide range of wired and/orwireless communications protocols, including, for example, a digitalmultiplexer (DMX) interface protocol; a Wi-Fi protocol; a Bluetoothprotocol; a digital addressable lighting interface (DALI) protocol; aZigBee protocol; a near field communication (NFC) protocol; a local areanetwork (LAN)-based communication protocol; a cellular-basedcommunication protocol; an Internet-based communication protocol; asatellite-based communication protocol; and/or a combination of any oneor more thereof. As described more below, coordinator 110 may beconfigured to use information from utility 106 or other sources (e.g.,available/forecasted supply, pricing signals, etc.) to determine whetherto grant or deny requests from DERs 102 to receive from or provide toutility 106 packets of energy.

The present inventors previously proposed PEM for coordinated chargingof plug-in electric vehicles (PEVs) in, for example, U.S. Pat. No.10,256,631, titled Systems and Methods for Random-Access PowerManagement Using Packetization, issued Apr. 9, 2019, (the '631 patent)which is incorporated by reference herein in its entirety. The '631patent discloses, inter alia, PEVs configured to asynchronously requestthe authority to charge with a specific probability according to theircorresponding state in a probabilistic automaton. For example, for athree-state finite-state machine, the probability to request access tothe grid from state i is P_i and P_1>P_2>P_3. If there is capacity inthe grid, the PEV is granted authority by a PEM coordinator to charge,but only for a fixed duration of time (e.g., 15 minutes), referred toherein as the control epoch, and in some examples, a state transitiontakes place: P_(i)→P_(i−1), which reduces the mean time-to-request byincreasing the magnitude of the request probability, P. In contrast, insome examples, if the PEV is denied authority to charge, the meantime-to-request increases by decreasing the magnitude of the requestprobability with a transition P_(i)→P_(i+1).

The present disclosure includes PEM techniques that may be used withloads whose operations, including request probabilities, are configuredto change based on locally-sensed conditions. For example, in someembodiments of the present disclosure, a thermostatically controlledload (TCL) may be configured to make requests for packets of poweraccording to a probability profile, the probability profile defining aprobability a DER will make a request to receive or provide power duringa particular communication epoch, wherein the probability profile is afunction of, e.g., a locally-sensed condition, such as local temperatureor state of charge. As described more below, other non-limiting examplesof locally-sensed conditions include pressure, e.g. for compressoroperations, voltage and state of charge for battery storage systems,etc. It should be noted that exemplary embodiments directed to TCLs areprovided for illustrating the disclosure, and absent an expresslimitation, the scope of the disclosure is not to be limited to TCLs.

FIG. 2 is a functional block diagram of one an example instantiation ofcoordinator 110 and one of DERs 102. As will be appreciated, althoughonly one DER is illustrated, coordinator 110 may be communicativelycoupled to a plurality of DERs for PEM control of the DERs. Similarly,although only one coordinator 110 is illustrated, a plurality ofcoordinators may work independently or collaboratively to manage DERs102. DER 102 is configured to request receipt or provision of electricalpower from coordinator 110 during communication epochs. In theillustrated example, DER 102 includes a packetized energy management(PEM) controller 220 that may include a coordinator interface 212 forcommunication with coordinator 110 using any wired or wirelesscommunication protocol known in the art. As described more below, any ofa variety of energy consuming or generating devices may be modified suchthat it can be control in a packetized manner using techniques disclosedherein by modifying the device to include PEM controller 220 orotherwise operably connecting a device to a PEM controller. For example,PEM controller 220 may be a physical device co-located with acorresponding DER 102, such as near or incorporated in a DER, such as ahot water heater, EV charger, etc. or may be implemented in a softwareservice, e.g. a software agent or software as a service. For example, aPEM controller 220 may be implemented in the cloud for remotely managingone or more DERs 102.

Coordinator interface 212 may be configured for any communicationprotocol known in the art via wired and/or wireless communication. Insome embodiments, for example, coordinator interface 212 is configuredfor power line communication with coordinator 110, e.g., using acommunication protocol that is transmitted/received over power lines.PEM controller may store one or more communication epoch values 222 andone or more control epoch values 224 in a memory 214. Communicationepoch, as used herein, may refer to a length of time between requestsmade by a DER 102 to the coordinator 110. Control epoch, as used herein,may refer to a length of time a DER draws power from or discharges powerto the grid. In some examples, the communication epoch for one or moreDERs 102 is fixed and predetermined. In other examples, one or both ofthe control epoch values 224 and/or communication epoch values 222 mayinclude a plurality of values. A processor 218 of PEM controller 220 maybe configured to select one of the plurality of communication epoch orcontrol epoch values. In yet other examples, coordinator 110 may beconfigured to change the communication epoch for one or more of DERs102. For example, coordinator 110 may include a memory 230 that includesone or more communication epoch values 232 or otherwise have access toone or more communication epoch values 232 (e.g. from utility 106) thata processor 234 of the coordinator may be configured to transmitindividually or via a broadcast to one or more DERs 102, e.g., via acommunication interface 236. Coordinator 110 may similarly have one ormore control epoch values 238 stored in memory 230 or otherwise haveaccess to such control epoch values and may transmit a specified controlepoch value to one or more DERs 102. For example, in some embodiments,one or more specified values for a communication epoch and/or a controlepoch may be sent from coordinator 110 to one or more DERs 102individually or via broadcast communication to reduce or increase thecommunication or control epoch of one or more DERs.

Memory 214 of PEM controller 220 may also store one or more probabilityprofiles 226 that, as described more below, define a probability a DER102 will make a request to receive or provide power during a particularcommunication epoch. PEM controller 220 may also include or becommunicatively coupled with one or more sensors 216 for measuring oneor more locally-sensed conditions, for example, a temperature, apressure, a revolution rate, a state of charge, a time-based deadline,or any other condition. For example, a DER 102 may include a temperaturesensor to measure the temperature—e.g., a hot water heater may include asensor to measure a temperature of the hot water stored within a tank ofthe heater. PEM controller 220 may be configured to receive more thanone locally-sensed conditions from more than one sensor 216, forexample, a temperature and a state of charge.

PEM controller 220 may also include a processor 218 that may beprogrammed to execute a PEM application 228 that includes instructionsfor selecting a request probability profile 226 and determining arequest probability for a given communication epoch from, for example,the selected request probability profile 226 and a locally-sensedcondition from one or more sensors 216. The request probability may be,for example, a probability that a request will be made during a givencommunication epoch. In a more specific example, the request probabilitymay be a charge request probability that a request for an energy packet(a charge request) will be sent to the coordinator 110. A givendetermined probability may be determined based on a selected probabilityprofile 226 and one or more locally-sensed conditions. Coordinator 110may store one or more probability profiles 240 in memory 2030 orotherwise have access to one or more probability profiles fortransmission to one or more DERs 102 for controlling the probabilityprofile applied by a DER. In other examples, Coordinator 110 may includean index that corresponds to probability profiles, for example,high/medium/low and/or AM/PM, and/or summer/fall/winter/spring, etc. andmay be configured to instruct one or more DERs 102 to shift to or selectthe corresponding probability profile stored in DER memory 214. Forexample, in the case of varying types of DERs 102, e.g., different typesof water heaters, air conditioners, freezers, EV chargers, batteries,each DER may store a plurality of probability profiles 226, such ashigh/medium/low that are specific to the operating characteristics ofthe DER. Coordinator 110 may send a broadcast to all or a subset of DERs102 to select one of its corresponding probability profiles, e.g., aninstruction to shift to a low probability profile, thereby reducingrequest probabilities applied by all or a subset of DERs, for example,during a power shortage period.

In some embodiments, the request probability approaches 1 as thelocally-sensed condition, e.g., a temperature, T, reaches a lowerthreshold, T_low, and the request probability approaches 0 when Tapproaches an upper threshold, T_high. As described below, a DER 102 maybe configured to “opt-out” of requesting energy packets from coordinator110 when T reaches T_low and instead transition to a traditionalnon-packetized operating mode where power consumption is based solely oncomparison of a locally sensed condition to a control band and withoutreference to a request probability or communication with PEM coordinator110. In other embodiments, a request probability, e.g., a dischargerequest probability may approach 1 as a locally-sensed condition, suchas state of charge, reaches a high threshold, and the requestprobability approaches 0 as the sensed condition approaches a lowthreshold. The DER may also be configured to opt-out of requestingenergy packets when T reaches T_high.

DER 102 may be further configured to receive a response to a request.For example, in some embodiments, DER 102 is configured to receiveapproval from coordinator 110 for a request for an energy packet. Insome examples, DER 102 may then change states and/or change to adifferent probability profile 226 based upon the response. For example,on an approved request, a DER state may change from a first state to asecond state. In another example, the DER state may change from a secondstate to a first state. Other cases exist for DERs with more than twostates and will be apparent in light of the present disclosure. The DER102 may be further configured to access electrical power based on thereceived response. For example, on approval of the requested energypacket, the DER may access electrical power for a period of timereferred to herein as a control epoch. In other examples, DER 102 maynot change states and instead maintain the same probability profile 226whether coordinator 110 accepts or denies a request.

If a DER determines during a communication epoch that it will request todraw an energy packet from, or discharge an energy packet to, anelectrical grid, the DER may send a request to the coordinator, or theDER may check a value of a broadcast signal generated by thecoordinator. For example, coordinator 110 may directly communicate withindividual DERs 102 and grant or deny requests from the DERs as therequests are received by the coordinator. In some examples, thecoordinator may instead broadcast an accept or deny message that DERs102 can monitor to determine whether to draw or discharge power. In someexamples, a broadcasted message from coordinator 110 may include aplurality of grant or deny messages, each of the plurality of grant ordeny messages being directed to one or more specific DERs 102. Forexample, a first region of an electric grid may have a power surplus anda second region of the electric grid may have a power deficit. Thecoordinator broadcast may include a grant message that includes anidentifier that corresponds to DERs connected to the first region and adeny message that includes an identifier that corresponds to the DERsconnected to the second region. Coordinator 110 may be configured toprovide ancillary services to an electrical grid by continuouslygranting or denying requests and/or broadcasting grant/deny requests inresponse to long-term, medium-term, and short-term needs. For example,day ahead load forecasting, minutes ahead pricing, and real-timefrequency regulation.

Coordinator 110 may also include a virtual battery model 242 that modelsone or more of the DERs 102 controlled by the coordinator as a “virtualbattery” by modeling the aggregate stored energy of the DERs and in someexamples, a prediction of a future energy levels. The stored energy or“state of charge” can also be viewed in the inverse as each DER'scurrent or forecasted “need for energy” (NFE). Non-limiting examples, ofa need for energy include temperature, state of charge (for a battery),a prediction of future usage requirements, the cleanliness of water in apool for a pool pump or a hot tub, the predicted need for charge in anupcoming driving event for an EV, or anything else in which sensors,user inputs and historical data indicate that there is a current orfuture need for energy. In one example, the coordinator does not need toreceive detailed information on the state of energy from each DER suchas the preceding non-limiting examples. Instead, in one example, theonly information the coordinator receives from the DERs is the rate ofrequests to receive or discharge energy packets. The coordinator maythen be configured to execute a virtual battery model and estimate thefleet NFE. Thus, the coordinator can model or estimate the NFE of thefleet of DERs while maintaining the anonymity of the members of thefleet. In another example, a coordinator is configured to use additionaldata collected from DERs other than request rate to estimate and developparameters for a virtual battery model of the fleet of DERs.Non-limiting examples of information received from DERs includes theamount of power the DER is discharging or drawing, a DER's target setpoint and current value (e.g., a target or setpoint temperature and acurrent temperature), or a current operating mode of the DER (e.g.,Normal or Eco or Performance mode). For example, virtual battery model242 may include the following:

E[k+t]=η₁ E[k]+η₂ P _(c)[k]Δt+η ₃ P _(d)[k]Δt+η ₄ P _(u)[k]Δt E_(mind)[k]≤E[k]≤E _(max)[k]  Eq. (1)

wherein:

E[k] is a measure of the fleet-wide energy storage at time k;

η₁, η₂, η₃, η₄ are efficiency-like constants learned from data and/orcomputational models;

Emin and Emax are model parameters indicating the upper and lower limitson energy storage for the virtual battery;

Pc[k] is the charging power, or the power that the coordinator allowsthe DERs as a fleet to consume. In examples where the virtual batterydoes not include batteries, or Pd[k]<0 this value may correspond to thebalancing reference signal e(t) (see, e.g., FIG. 6A) for the virtualbattery.

Pd[k] is the discharging power, or the power the coordinator allows theDERs as a fleet to discharge into the grid; and

Pu[k] is the power the DERs are using internally (for example the waterheaters using hot water from the tank). In one example, Pu[k] is derivedfrom available data and models, for example, averaged baseline behavior.

The Coordinator may use a wide range of data to estimate the parametersof the virtual battery model, including weather information, such asambient temperature data, information about requests from DERs, totalpower used by charging and discharging DERs, and data sent by the DERsto the coordinator. Model parameters may be initiated at best estimateor nominal values and be modified over time and stored in memory 230.Coordinator 110 may be configured to use virtual battery model 242 tocompute an optimal trajectory for the fleet of DERs (the “virtualbattery”), which may be used to determine an estimate of a set pointpower, P[k] for all k greater than the current time, where set pointpower is Pc[k]−Pd[k]. As described more below, coordinator 110 may beconfigured to continuously adjust the set point power in response tosignals from the utility and to continuously accept or deny charge anddischarge requests to track the set point power. Virtual battery model242 may also separately model the aggregate energy storage and futureenergy needs of a subset of DERs located in specific geographic regionsor connection to certain segments of an energy grid for localized PEMmanagement of a subset of DERs.

Coordinator 110 may also include one or more a reference filters 244. Asdescribed more below, reference filter 244 may be used to filter areference signal from a utility to shape, limit, or otherwise modify theutility signal to, for example, optimize the long-term performance of afleet of DERs 102.

FIG. 3 illustrates an example instantiation of system 100 (FIG. 1) inthe form of system 300. A DER 102 is physically connected to adistribution network 304 of a utility and communicatively coupled to aPEM controller 220. PEM controller 220 is communicatively coupled with acloud-based DER management system 308 that may include one or more PEMcoordinators 110 (see FIG. 2). Management system 308 is configured toreceive reference signals 310 from a grid operator 312 and coordinateflexible energy resources (DERs 102) to track the balancing referencesignal. As shown, management system 308 may also be able to communicatewith grid operator 312, for example with information 314 on theavailability of DERs 102. Grid operator 312 also receives information316 on the state of the distribution network 304 such as such asvoltage, frequency, and power flows.

DER management system 308 may include a plurality of coordinators 110,each coordinator in communication with a corresponding fleet of DERs,for example, in a corresponding geographic region. For example,distribution network 304 may have any topology known in the art, such astransmission, distribution, feeder, and neighborhood levels. Managementsystem 308 may include a plurality of coordinators 110 for controllingDERs connected to specific portions of the grid. Similarly, referencesignals 310 may include reference signals for specific sections of thegrid so that the coordinators can separately control DERs in differentgeographic regions so that the net energy load of the DERs located orotherwise electrically proximate each region tracks the correspondinggrid reference signals 310 for each region. As described more below,management system 308 may also receive directional predictions of thereference signals, which may also be dependent on geography so thatcoordinators 110 can leverage local grid predictions to manage ageographic network of DERs. In another example, a coordinator 110 may beconfigured to control multiple regions of DERs connected tocorresponding multiple regions of distribution network 304 by comparingthe requests for a specified region of DERs to a correspondingregion-specific reference signal. For example, DERs could include aregion ID in their requests, the coordinator could model each region asa separate virtual battery, and the coordinator could similarly receiveregion-specific reference signals from the utility for granting ordenying requests.

Traditional Control of TCLs

The vast majority of traditional TCLs operate in a binary (ON/OFF)manner and are already controlled by simple state machines—thermostatsthat change state based on temperature thresholds. Locally, a TCL iscontrolled to maintain a desired temperature set-point, T_(n) ^(set),within a temperature dead-band, T_(n) ^(set)±T_(n) ^(set,DB)/2. Thisyields the standard TCL hysteretic temperature response according tolocal discrete-time control logic:

$\begin{matrix}{{z_{n}\lbrack k\rbrack} = \left\{ {\begin{matrix}{1,} & {{T_{n}\lbrack k\rbrack} \leq {T_{n}^{set} - {T_{n}^{{set},{DB}}/2}}} \\{0,} & {{T_{n}\lbrack k\rbrack} \geq {T_{n}^{set} - {T_{n}^{{set},{DB}}/2}}} \\{{z_{n}\left\lbrack {k - 1} \right\rbrack},} & {otherwise}\end{matrix}.} \right.} & {{Eq}.\; (2)}\end{matrix}$

The aggregate response under the above fully-decentralized control logicis referred to herein as the “no-control” case. The PEM schemesdisclosed herein may be implemented by simply replacing the existingstate machine with a more sophisticated one—e.g., PEM controller220—that interacts with a coordinator 110.

Example Application of PEM to TCLs

FIG. 4 shows an example control of a DER 102 in the form of a waterheater managed by an exemplary embodiment of a PEM controller 220. Theleft figure shows a sequence of events. At time t_a, when grid resourcesare unconstrained, the DER stochastically requests (R) or does notrequest (N) energy. At t_b, the system approaches a period ofconstrained supply, in which a system coordinator mostly denies requests(D) and reduces the control epoch length to reduce the amount of energydrawn during any one control epoch. As a result, in one example, the PEMcontroller transitions to a lower probability state or profile (e.g.,e.g., P₁→P₂). In another example, a PEM controller may not changeprobability profiles in response to an accept or deny response from acoordinator. At t_c, the temperature hits a QoS bound 402 and the loadexits (X) from PEM and rapidly seeks to recover temperature to withinthe QoS bounds, which occurs at t_d. The right portion of FIG. 4illustrates one example of a PEM controller changing requestprobabilities (P_(i)(T)) and, in some examples, its control and/orcommunication epoch lengths.

FIG. 4, therefore, illustrates the operation of an example DER in theform of an electric furnace or water heater or other device configuredto consume electric power to control a temperature. When thelocally-sensed condition, here local temperature, T, of the TCL, isbetween its upper 404 and lower 402 temperature limits for PEMoperation, the TCL's time-to-request may be driven by, for example, aprobability profile that defines request probability as a function oflocally-sensed conditions. In some examples, as described more below,the probability profile may be an exponential distribution whose mean isinversely proportional to T relative to the upper limit, T_high. Forexample, TCLs with temperatures very close to the lower threshold makerequests with near certainty (i.e., P_(i)(T→T_(low))≈1) and those nearthe upper limit in temperature will make requests with low probability(i.e., P_(i)(T→T_(high))≈0).

Upon transmitting a request and, if there is capacity in the grid, theTCL will be given the authority to turn ON for a fixed control epochlength δ_(t) (i.e., z_(n)(t)=1 for t∈(t₀, t₀+δ_(t))), and in someexamples, a state transition occurs: P_(i)(T)→P_(i−1)(T). If the requestis denied, in some examples, the TCL finite state machine may transitionto a state with lower mean time to request (MTTR), P_(i)(T)→P_(i+1)(T),but may immediately resume requesting with temperature-dependentprobability. If access is denied repeatedly, T reaches T_(low) 402,which causes the TCL to exit (i.e., opt-out of) the PEM scheme toguarantee that temperature bounds are satisfied. An illustrative ON/OFFcycle of a packetized water heater is illustrated in FIG. 4 (left). Notethat the illustrative control depicted in FIG. 4 would be reversed ifthe DER was a cooling DER (i.e., a DER managing a cooling DER such as,for example, a freezer, etc.)

In addition to the TCL receiving an “allow/deny” response to a request,the TCL may also receive an updated (global) control epoch length,δ_(t), thus enabling tighter tracking in the aggregate, which is helpfulduring ramping events. In the example, while a TCL is ON, it does notmake requests.

In one example, a plurality of DERs in the form of TCLs may beconfigured to operate in the manner illustrated in FIG. 4, and a DERcoordinator (e.g., coordinator 110) may be configured to grant or denythe authority to turn on without requiring any knowledge or tracking ofparticular DERs. Furthermore, in some examples, the coordinator doesneed to track which TCL is making a particular request. As each TCL iscontrolled by its corresponding PEM controller and probability profile,and its ability to turn on depends only on the real-time systemcapacity, in one example, any TCL making a request at the same point intime will be treated the same by the coordinator. As such, PEM controlschemes disclosed herein maintain privacy while still being fair tocustomers. As described more below, PEM approaches disclosed herein canbe agnostic to the types or mix of TCLs being coordinated. That is,electric water heaters and air conditioners can be managed on the samesystem. The quality of service for customers can be maintained througheach device's ability to temporarily “opt-out” of PEM when the device'scondition falls out of the deadband (e.g., between T_low 402 and T_high404).

In a discrete-time implementation of PEM of a DER, the probability thatDER n with local condition C_(n)[k] in automaton state i requests accessto the grid during time-step k (over interval Δt) may be defined asP_i(C_n[k])=F(Cn[k], i), where the function, F, can vary depending on aparticular application. One example application for TCLs is as follows.

Example Probability Profile for TCLs

In a discrete-time implementation of PEM for a DER in the form of a TCL,the probability that TCL n with local temperature T_(n)[k] in automatonstate i requests access to the grid during time-step k (over intervalΔt) may be defined by the cumulative exponential distribution function:

P _(i)(T _(n)[k]):=1−e ^(−μ(T) ^(n) ^([k],i)Δt)  Eq. (3)

where rate parameter μ(T_(n)[k], i)>0 is dependent on the localtemperature and the probabilistic automaton's logic state i. Thisdependence is established by considering the following boundaryconditions:

1. P_(i)(TCL n requests access at k|T_(n)[k]≤T_(i) ^(min))=1

2. P_(i)(TCL n requests access at k|T_(n)[k]≥T_(i) ^(max))=0,

which give rise to the following design of a PEM rate parameter:

$\begin{matrix}{{\mu \left( {{T_{n}\lbrack k\rbrack},i} \right)} = \left\{ \begin{matrix}{0,} & {{{if}\mspace{14mu} {T_{n}\lbrack k\rbrack}} > T_{n}^{\max}} \\{\frac{T_{n}^{\max} - {T_{n}\lbrack k\rbrack}}{{T_{n}\lbrack k\rbrack} - T_{n}^{\min}}M_{i,}} & {{if}\mspace{14mu} {T_{n}\lbrack k\rbrack}{\epsilon\left( {T_{n}^{\min},T_{n}^{\max}} \right.}} \\{\infty,} & {{{if}\mspace{14mu} {T_{n}\lbrack k\rbrack}} \leq T_{n}^{\min}}\end{matrix} \right.} & {{Eq}.\; (4)}\end{matrix}$

where M_(i)>0[1/sec] is a design parameter that depends on the TCL'sautomaton state i and defines the MTTR. For example, if one desires aMTTR of 5 minutes,

$M_{i} = {\frac{1}{600}{{Hz}.}}$

For a symmetric dead-band, e.g., T_(n) ^(min), T_(n) ^(max):=T_(n)^(set)∓T_(n) ^(set,DB)/2, then the mean time-to-request (MTTR) for TCL nwith T_(n)[k]=T_(n) ^(set) is exactly described by 1/M_(i) (in seconds),which represents a useful parameter for design of the finite-statemachine. FIG. 5 illustrates three example probability profiles 500—P1,P2, P3. In the illustrated example,P₁(T_(n)[k])>P₂(T_(n)[k])>P₃(T_(n)[k]). In the case of asymmetricdead-band, M_(i) can still be an effective design parameter bygeneralizing the middle condition of Eq. (4):

$\begin{matrix}{{\mu \left( {{T_{n}\lbrack k\rbrack},i} \right)} = {\left( \frac{T_{n}^{\max} - {T_{n}\lbrack k\rbrack}}{{T_{n}\lbrack k\rbrack} - T_{n}^{\min}} \right)\left( \frac{T_{n}^{set} - {T_{n}^{\min}\lbrack k\rbrack}}{{T_{n}^{\max}\lbrack k\rbrack} - T_{n}^{set}} \right){M_{i}.}}} & {{Eq}.\; (5)}\end{matrix}$

FIG. 5 illustrates example probability profiles 500 implementingEquation 3, above, and shows three example probability profiles P1, P2,and P3 as a function of a locally-sensed condition, here the watertemperature of the device. FIG. 5 also shows low and high temperatureboundaries 504, 506. FIG. 5 also shows MTTR profiles 502 for P1, P2, andP3, which in the illustrated example, illustrate the inverse relation tothe corresponding probability profile. For graphical purposes only, themean time-to-request have been truncated to 40 minutes. In theillustrated example, each of the probability profiles approach a valueof 1 or 100% as the temperature drops to low temperature boundary 504and decreases to zero as the temperature approaches the high temperatureboundary 506. The MTTR profiles represent the average time betweenrequests associated with a given probability value over a large numberof communication epochs. Thus, as the probability of making a requestdecreases, for example, as temperature increases, the length of timebetween requests will increase. As will be appreciated, in otherexamples, probability profiles may be defined by equations other thanexponential curves, such as one or more of linear, sinusoidal, squarewave, step change, etc. In some examples, a probability profile may be asingle value that is independent of any locally-sensed condition.Probability profiles P1, P2, and P3 may be associated with differentmodes of operation, for example, P1, defines higher probabilities as afunction of temperature than curves P2 and P3 and are examples ofprobability profiles 226 and 240 (see FIG. 2). In other examples,probability profiles may intersect. For example, P_a(T1)>P_b(T1) butP_a(T2)<P_b(T2).

As described above, a memory of a PEM controller such as memory 214 ofPEM controller 220 may store a plurality of probability profiles 226,such as profiles P1, P2, and P3, and the PEM controller processor 218may be configured to first determine which profile to apply and thendetermine a specific probability, for example, according tolocally-sensed conditions. The PEM controller may be configured toselect one of a plurality of probability profiles based on any of avariety of factors or inputs. For example, the PEM controller 220 mayshift to a lower probability profile, e.g., from P1 to P2, in responseto a coordinator denying the controller's request to consume a powerpacket. In some examples, the controller may receive a command from acoordinator to shift probability profiles, or to select a specificprobability profile, or a controller may receive a probability profilefrom a coordinator for storage in memory and for application by thecontroller. For example, in response to an energy shortage, acoordinator may send a PEM control signal as individual commands or abroadcast that one or more DERs shift to a lower probability profile.

Referring again to FIG. 4, with the stochastic nature of TCLs under PEM,it is entirely possible that a disturbance (e.g., a large hot waterwithdrawal rate) can drive T_(n)[k] below T_(n) ^(min). Therefore, tomaximize quality of service to the consumer (i.e., avoid cold showers),in some embodiments of the present disclosure, a TCL under PEM cantemporarily exit (i.e., opt-out of) PEM and operate under traditionalTCL control (e.g., turn ON and stay ON). This is illustrated in FIG. 4(left) at event t_(c) and with HEAT/OFF automaton states in FIG. 4(right). That is, once a TCL under PEM exceeds temperature bounds,traditional control logic may temporarily be employed to bring the localtemperature within PEM “recovery bounds” T_(n) ^(set)±T_(n) ^(set,PEM)/2with T_(n) ^(set,PEM)<T_(n) ^(set,DB) when PEM control logic isreinstated (i.e., TCL opts back into PEM). The recovery bounds arehelpful to avoid excessive exit/re-entry cycling at the min/max bounds.While cold showers are undesirable, overheating hot water heaters can bedangerous to consumers and damaging to the water heaters. As such, a TCLunder PEM may be configured to never actuate if T_(n)[k]>T_(n) ^(max).

Example PEM Control Schemes

FIGS. 6a-6c illustrate examples of PEM control schemes of the presentdisclosure.

FIG. 6A is a diagram depicting a closed-loop feedback system 600 for PEMwith a reference r(t) (in one example, reference r(t) corresponds todispatch signal 310 (FIG. 3) that may be provided by a grid operator(e.g. grid operator 312 (FIG. 3) and an aggregate system output responsey(t) measured by grid operator 312 (FIG. 3) which is the real-time netpower load of both the controllable packetized DERs 102_1-102_n that areparticipating in PEM (e.g., that have not “opted out” as describedherein) and the net load of uncontrollable loads 602, which can includeDERs that have opted out as well as other non-PEM loads. In one example,y(t) is provided to management system 308 by grid operator 312, forexample, the grid operator can measure total real-time demand at one ormore locations of distribution network 304, for example, at one or morefeeder and/or substation locations, and provide the real-time demand tomanagement system 308. Reference r(t) can be a voltage reading,available supply signal, energy pricing information, etc.

Referring again to the exemplary embodiment of FIGS. 2 and 3, managementsystem 308, which may include one or more coordinators 110 (FIG. 2) thatreceive balancing commands in the form of reference signal r(t) fromgrid operator 312 and coordinates flexible energy resources (DERs 102)to track the balancing command. In control system 600, one or morecoordinators 110 are configured to track reference signal r(t) byresponding to load access requests 604 from DERs with “Yes” or “No”notifications based on a real-time output error between real-time netpower load, y(t), and the reference signal, r(t): e(t):=r(t)−y(t). Inone example, coordinator 110 may be configured such that if e(t)>0 then“Yes”; else “No.” In one example, coordinator 110 may be configured forthe following inputs and outputs:

Input: Balancing reference signal, e(t)

Output: Yes/No access notification; control epoch length.

The transmission (e.g., ISO New England) or distribution utility systemoperator (e.g., a DSO Control Room (grid operator 312, FIG. 3) may beable to measure or estimate the states of distribution network 304, suchas voltage, frequency, and power flows. Under scenarios with highpenetration of renewable energy, the grid operator will find it evermore difficult to balance demand and supply and will be able to leveragethe flexible packetized DERs sitting in customer homes andindustrial/commercial facilities as described herein. This can beachieved by signaling individual balancing commands via individualreference signals, r(t) to coordinator 110 across the distributionnetwork 304 in near real-time, akin to Automatic Generator Control (AGC)signals, which are transmitted every 4-5 seconds today. Thus, gridoperator 312 may be summarized by the following inputs and outputs:

Input: Grid states and net-load forecasts;

Output: Balancing request signal, r(t)

By managing anonymous, fair, and asynchronous requests for packetizedloads via a coordinator that receives grid or market-based balancingsignals from a grid operator, PEM represents a bottom-up distributedcontrol scheme.

Grid operator 312 may determine reference signals r(t) based on multipletime scales, for example, (1) long term (e.g., day ahead load)forecasting to curtail loads for peak load reduction purposes; (2)medium term (e.g., minutes ahead pricing) to encourage/dissuaderesources to/from operating based on the signal, which may be aneconomic one (price); and (3) real-time (e.g., second by second) toencourage/dissuade resources operating for frequency regulation or otherancillary grid services. The reference signals r(t) may be distributedat any location in the distribution network, such as at thetransmission, distribution, feeder, and/or neighborhood levels of thenetwork.

Grid operator 312 may receive a plurality of external signals as inputfor determining reference signals r(t), for example, economicinformation from the system (prices, cost of generation, etc.);information about the state of the distribution network 304 (highlyloaded network components, etc.), information about the probability of atime period being a peak load event, and forward looking forecast for ofany of the preceding external signals.

FIG. 6B illustrates another example of a closed-loop feedback system inthe form of a predictive closed-loop feedback system 620, which is amodified version of system 600 (FIG. 6A) to incorporate a look-aheadperiod to change the ‘Yes rate’ by coordinator 110 configured withpredictive virtual battery control. Specifically, coordinator 110 mayreceive a prediction of the reference signal, r(t) and an estimated needfor energy (NFE) of all DERs 102 controlled by the coordinator, asmodeled by virtual battery model 242 (FIG. 2). As noted above, the NFEmay be a measure of an aggregate amount of energy currently stored bythe DERs and/or a prediction of a future need for energy by the DERs.Thus, as with control system 600, in control system 620, grid operator312 provides a reference signal r(t) which is compared to real-time netpower load, y(t), to determine a balancing reference signal, e(t).Coordinator 110 is configured to receive directional predictions 622from grid operator 312 on future changes in reference signal, r(t), onone or both of long-term and medium-term time scales. Non-limitingexamples of directional predictions of a power grid reference signal,r(t), are an automatic generation control signal, a spinning reservesdispatch signal, and an inverse of wholesale energy prices. Coordinator110 may be configured to process predictions 622 and fleet NFEinformation 624 from virtual battery model 242 to determine whether toaccept or deny specific requests 604 in view of a current value ofbalancing reference signal e(t). For example, balancing reference signale(t) may indicate a nominal amount of energy is available for grantingpower requests, however, virtual battery model 242 may indicate theaggregate NFE of the DERs is relatively low and prediction 622 mayindicate that a period of energy shortage is approaching. Managementsystem 308 may decide to grant more power requests and deny moredischarge requests than it would otherwise given the current value ofbalancing reference signal e(t) in order to increase the aggregateenergy storage of the fleet of DERs in advance of the expected energyshortage.

FIG. 6C illustrates yet another example of a closed-loop feedback systemin the form of a predictive-precompensator closed-loop feedback system640. System 640 applies a reference filter 244 (see also FIG. 2) thatfilters reference signal r(t). Reference filter 244 may be configured asa pre-compensator with features similar to the predictive virtualbattery model control features of coordinator 110 in FIG. 6B.Specifically, reference filter 244 may receive statistics andpredictions 642 of the reference signal, r(t) and an estimated NFE 624of all DERs 102 controlled by the coordinator, as modeled by virtualbattery model 242. Filter 244 may be configured to modify referencesignal r(t) to optimize tracking over time. For example, filter 244 mayprovide a low-pass response so that e(t) captures the variability in theuncontrollable load (e.g., when renewables are fluctuating). A low-passfilter can help avoid having the fleet of DERs, acting in aggregate as avirtual battery, from jumping up and down to chase large, but transientchanges in r(t). In another example, filter 244 may provide a high-passfilter where the DERs acting as a virtual battery compensate for thevariability in r(t). In another example, filter 244 may be configured tore-shape reference signal r(t) to optimize the capabilities of the fleetof DERs (the virtual battery, VB) over a given timeframe, e.g., tooptimize long-term performance of the VB. As one example, r(t) may be amoving average and autocorrelation over a preceding timeframe, e.g., 5minutes, of a grid-supplied frequency regulation signal. Coordinator 110and/or reference filter 244 may also receive VB model information (fleetNFE information 624), e.g., energy stored. This information can be usedby filter 244 to determine specifications of a finite impulse response(FIR) filter (e.g., bandwidth). Weighted least squares or other designmethods may be used to determine optimal FIR filter coefficients tosatisfy specifications.

Filter 244 is configured to output a filtered reference signal r(t)′,which can then be compared to real-time net power load y(t) to determinebalancing reference signal e(t). In the illustrated example, balancingreference signal e(t) is used by one or more coordinators 110 ofmanagement system 308 to grant or deny requests according to whether thevalue of e(t) is greater than or less than zero. As will be appreciated,features from the control architectures illustrated in any of FIGS.6A-6C may be combined in a variety of ways, for example, referencefilter 244 may be combined with the predictive virtual battery controlcoordinator 110 of FIG. 6B.

FIGS. 7A-C illustrate one example application of reference filter 244 inthe form of a low-pass filter on reference signal r(t) to limit a rateof change of balancing reference signal e(t) to prevent rapid increasesin charging after a long shortage period. FIGS. 7A-C illustrate theresults from an analysis of PEM performance under severe operatingconditions. More specifically, four versions of PEM control schemes,each in the illustrated example with a control epoch of 5 minutes, wereemployed for a load reduction event to investigate the rebound effect ofpacketized water heaters when all requests over a 6-hour period (minutes180-540) were rejected. The four cases shown include two cases, 704,706, where no reference filter was applied (referred to in FIG. 7A as“no ramp limit”), a case 704 with a MTTR of 30 seconds, and a case 706with a MTTR of 300 seconds, meaning case 706 had a lower requestprobability than case 704. And two cases 708, 710, where a referencefilter was applied, (referred to in FIG. 7A as VB ramp limit), a case708 with a MTTR of 30 seconds, and a case 710 with a MTTR of 300seconds. FIG. 7A also shows reference signal r(t) 702, which had a stepchange from 0 to approximately 5 MW at time 540.

7A illustrates a portion of this 6-hour reduction event. After the sixhours of the coordinator rejecting every request, the water heaters areallowed full access to the grid (i.e., all requests are accepted by acoordinator). This scenario can be thought of as an extreme peakreduction event by a utility or load aggregator. In the illustratedexample, while the coordinator declined all packet requests, most or allof the PEM-controlled water heaters eventually opted out of PEM tomaintain QoS. Thus, the only power consumed during the 6-hour period wasfrom water heaters that had opted out. In the illustrated example, whena water heater's temperature dropped belowT_n_set−T_n_set_PEM=0.92×T_n_set, meaning the water heater's temperaturewas measured to be in the zone that is below the consumer's preferences,the heater automatically turns on (w/o permission from the coordinator.As illustrated in FIG. 7A, during the load reduction period (prior tominute 540), the aggregate opt-out consumption in the example stabilizedaround 1000 kW. However, during this opt-out period, large groups ofwater-heaters naturally become synchronized and, at minute 540, when thereference abruptly changes, the coordinator can experience largeMW-scale (damped) oscillatory ramping events as shown in FIGS. 7A and 7Bfor the “No ramp limit” cases 704 and 706 where the coordinator justaccepts all incoming requests. The large oscillations occur at a periodequal to the control epoch until the randomizing nature of PEMdesynchronizes the population. Note that shorter MTTR (and acorresponding higher request probability) begets increased oscillationsas more frequent requests prevent desynchronization.

To prevent this large spike, the coordinators in cases 708 and 710 wereequipped with a reference filter 244 that included a ramp-rate limit,e.g., 300 kW per minute, which effectively limits the number of packetsthat can be accepted during an interval (e.g., 60 packets per minute).As displayed in FIGS. 7A and C, the coordinator ramp-rate limitreference filter is clearly successful in mitigating the rebound effectas it prevents re-synchronization of packetized loads between controlepochs and limits the initial rebound peak by about 75%. A potentialdrawback of a ramp-rate limit is a longer recovery period, which couldimpact future availability of PEM for tracking or additional peakreduction services. In another example, coordinator 110 may also beconfigured to broadcast a standby command to the fleet of DERsinstructing any DER with a locally-sensed condition above a minimumthreshold to transition to a standby mode and not make a request forpower. For example, any water heater with a temperature above a minimumvalue should standby and not request a power packet until furthernotice. Such a standby command broadcast can be sent immediately priorto the end of a long shortage period to minimize the number of DERs thatdraw power after the shortage period ends and the utility referencesignal r(t) increases. A standby command can be used in combination withor separately from reference filter 244. In other examples, where thereis an energy surplus, or coordinator 110 otherwise determines the fleetof DERs should draw power, coordinator 110 may be configured to send aPEM control signal that instructs all or a subset of DERs to requestpower at a greater rate than they currently are by shifting to a higherprobability profile, or by automatically requesting power during everycommunication epoch, i.e. a probability of 1, or by transitioning to aconstant power consumption mode.

In one example, a reference filter ramp-rate limit (RRL) may beimplemented with a moving window integrator (MWI) that adds up the powerfrom all accepted packet requests over a preceding Delta t time periodand compares the fleet of DER's (the “VB's”) actual ramp-rate (e.g.,kW/min) to the ramp-rate limit. If the VB is currently operating at orabove the VB ramp-rate limit, incoming packets will be denied even ifthe real-time net power load, y(t), is below a power grid referencesignal r(t) provided by, e.g., grid operator 312.

$\begin{matrix}{{{MWI}(t)} = {\frac{1}{\Delta t}{\sum\limits_{\tau = {t - {\Delta \; t}}}^{t}{P_{yes}\lbrack\tau\rbrack}}}} & {{Eq}.\; (6)}\end{matrix}$

Then, while/if MWI(t)≥RRL, then deny packet requests. Else, let acoordinator 110 determine whether to accept or deny request according toother logical conditions the coordinator is configured to evaluate. Forexample, the RRL may be part of a logical energy packet request decisionhierarchy that the coordinator is configured to execute to determinewhether packet requests should be accepted or denied. In one example, acoordinator is configured to accept an energy packet request whendecision hierarchy results in a yeses (or “green” lights) from ALLcoupled logical entities or conditions.

In another example, additional randomization may be injected into PEMcontrol schemes of the present disclosure by modifying the coordinator'sacceptance of packet requests. For example, in closed-loop feedbacksystem 600 of FIG. 6, coordinator 110 is configured to accept all powerpacket requests 604 that are received when balancing reference signale(t) is greater than zero. Coordinator 110 may be configured torandomize acceptances of packet requests 604, either only when balancingreference signal e(t) is greater than zero or also when the balancingreference signal is less than zero, to further randomize the fleet ofDERs. Any of a variety of inputs may be used for the randomization ofcoordinator acceptance, including external signals, analytical models,utility grid statistics, and predictions, such as prediction of prices,weather, or other relevant parameters. In another example, additionalrandomization to prevent synchronization can be achieved by applying aprobability of achieving a set point. Setting the probability high orlow would increase/decrease a ramp rate limit. For example, acoordinator 110 may determine the aggregate stored energy of a fleet ofDERs 102 as a whole should be increased but that there is a significantamount of time to do so (e.g., the coordinator has overnight to movewater heaters to a high temperature for morning use). Thus, thecoordinator may initial set a probability of achieving the set pointduring the night to a low value and then increase the value, the lowvalue corresponding to a lower ramp rate and a higher valuecorresponding to a higher ramp rate.

Thus, synchronization effects that can plague certain load controlschemes can be avoided with PEM control schemes of the presentdisclosure. As illustrated in FIGS. 7A-7C and described above, onetechnique for preventing synchronization after a long shortage period isto incorporate a low-pass filter that modifies a utility referencesignal r(t) with a reference filter 244 to limit the rate of increase ofthe reference signal. Coordinators made in accordance with the presentdisclosure may be configured with a variety of other mechanisms formaintaining randomization of DERs. For example, Coordinator 110 may beconfigured to send instructions to one or more DERs 102 to modify a PEMcontrol parameter of the DER to prevent synchronization. For example, acoordinator 110 may send instructions to one or more DERs 102 to varyone or more of the control epoch length 224, communication epoch length222, a minimum off time between control epochs, or the opt-in value fora locally-sensed condition (e.g., the temperature or charge where a DERthat has opted out of PEM because it has dropped below a QoS thresholdopts back in to PEM). In some examples, coordinator 110 may beconfigured with a random number generator that generates a random numberfor one or more of the preceding PEM control parameters, for example,within an upper and lower limit for each type of control parameter, andthose parameters can be randomly assigned to the DERs, resulting in afurther randomization of the DER PEM behavior.

Control of Bi-Directional Resources

In another aspect of the present disclosure, the bi-directional controlof a DESS is enabled using, e.g., two different probabilisticautomatons. Bi-directional resources like DESSs improve the ability of acoordinator to ramp down (via discharging). TCLs, by contrast aretypically not controllable to the same extent as they can only becontrolled to go down by rejecting requests. For example, if acoordinator declines a TCL packet request, the TCL doesn't ramp up butthe coordinator cannot control the rate of ramping down without having adelay in response. If the coordinator accepts a TCL packet request, theTCL ramps up and the coordinator can control the rate of ramping up in avariety of ways described herein, e.g., by saying “YES” to every request(assuming sufficient requests are incoming) thereby controlling the rateof ramping up with rate of acceptance. By contrast, a coordinator canaccept a DESS discharging request, resulting in a ramp down and thecoordinator can control the rate of ramping down with rate of acceptingdischarging requests. Further, a coordinator can accept a DESS chargingrequest resulting in a ramp up and the coordinator can control the rateof ramping up with the rate of accepting charging requests.

Thus, incorporation of PEM-controlled energy storage devices improves acoordinator's ability to ramp down a fleet of DERs. As such, PEM of afleet of DERs can improve with more heterogeneous loads—control improvesunder a diversity of loads. In an exemplary embodiment described below,electric battery storage is considered, however, the scope of thedisclosure is not limited to electric battery storage. Other examples ofDESS include any of a variety of other storage types such as, forexample, mechanical storage (e.g., pneumatic and hydraulic pumpstorage), electrical-chemical storage processes (e.g., electrolysis/fuelcell operation), etc. and any combination of different storage types.Similarly, language used throughout the present disclosure uses thevernacular of a battery storage system (e.g., “State of Charge”) forconvenience only, and the disclosure should not be limited toembodiments using only battery storage systems.

FIG. 8 top shows an example charge request probability profile 802 andan example discharge request probability profile 804 that one or moreDESSs may be configured to use. Charge and discharge profiles 802, 804define a request probability as a function of the DESS's dynamic state,for example, the DESS's state of charge in the case of a battery storagesystem. FIG. 8 also shows an example idle or stand-by probabilityprofile 806 that illustrates a probability that a DESS configured tooperate under both profiles 802, 804, may either decide to both requesta charge and discharge or decide to not request either a charge ordischarge, in both cases the DESS may not send any request to acoordinator during the corresponding communication epoch. In oneexample, a DESS configured to execute both profiles 802 and 804 may beconfigured to only make a request when one of its automatons determinesa request should be made. The lower graph in FIG. 8 illustrates a meantime to request a charge curve 807 and a mean time to request adischarge curve 808, each as a function of a DESS's dynamic state (e.g.,state of charge in a battery storage system).

In one example, a DESS may include a first automaton that determines aprobability that the DESS will request an energy packet from the grid(i.e., a “charge”)—similar to other PEM methods disclosed herein. Asecond automaton determines the probability that the DESS will requestto provide an energy packet to the grid (i.e., a “discharge”). In thecase of a battery, in one example, the probabilities are defined as afunction of the state of charge (SOC) of the DESS. To ensure a minimumSOC is maintained, DESS may also be configured with a charge threshold(C_thresh) 810, below which the first automaton will always request anenergy packet (i.e., probability is set to “1”) or alternately may optout and switch to a traditional charging method. Likewise, to allowexcess DESS energy to be sell back to the grid, there may be a dischargethreshold (D_thresh) 812, above which the second automaton's probabilityis set to “1.” Between the two thresholds, the DESS can, at eachcommunication epoch, request a charge, discharge, or standby (i.e., norequest). In one example, the first and second automatons may operateindependently, and if both a charge request and a discharge request aredesired in the same epoch, the DESS will standby (i.e., neither requestwill be sent).

Referring again to FIG. 2, in some embodiments, one or more DERs 102 maybe a DESS and may be operably coupled to a PEM controller 220 for PEMmanagement in connection with coordinator 110. Sensor 216 may provide acurrent state of charge accessible by processor 218. Probabilityprofiles 226 may include both charge and discharge probability profilesand processor 218 may be configured to execute PEM application 228 whichmay access one or both probability profiles for determining whether tomake a request during a given communication epoch. The chargeprobability profile is a charge request probability (i.e., theprobability that the DER will request a charge in the communicationepoch. The discharge probability profile is a discharge requestprobability (i.e., the probability that the DER will request todischarge in a communication epoch. The discharge request probabilitymay approach 1 as the DER's locally-sensed condition (e.g. state ofcharge) increases to a discharge threshold (D_thresh). The chargerequest probability may approach 1 as the DER's locally-sensed conditiondecreases to a charge threshold (C_thresh). The charge threshold is lessthan the discharge threshold (C_thresh<D_thresh). DER 102 may make acharge request based on the charge request probability and the state ofcharge condition and may be further configured to create a dischargerequest based on the discharge request probability and the state ofcharge condition. In some embodiments, when the charge requestprobability and the discharge request probability are such that both acharge request and a discharge request would be sent, the DER may beconfigured to send neither a charge request not a discharge request. Inother words, the DER is configured such that neither a charge requestnor a discharge request are created if the charge request probabilityand the discharge request probability would otherwise cause both to becreated.

Processor 218 may be configured to determine a charge requestprobability for a communication epoch, wherein the charge requestprobability approaches 1 as the state of charge decreases to the chargethreshold, C_thresh, and a discharge request probability approaches 1 asthe state of charge increases to a discharge threshold, D_thresh, whereC_thresh<D_thresh. The DER may be further configured to create a chargerequest with a determined probability (the charge request probability)based on the state of charge condition and create a discharge requestwith a different determined probability (the discharge requestprobability) based on the state of charge condition. If the charge anddischarge automatons either both create a request or both do not createa request then no request is forwarded to the coordinator. If only oneof the automatons creates a request then that request (charge ordischarge) is forwarded to the coordinator.

FIG. 9 further illustrates various states of a DESS configured for PEM.Referring to FIGS. 8 and 9, in the illustrated example, if a DESS's SOCis below C_thresh, e.g., 0.5, the DESS will request charge withprobability 1 and will not request a discharge. If the DESS's SOC isabove 0.5, it will independently request a charge according to chargerequest probability profile 802 and request a discharge according todischarge probability profile 804. If both actions are ‘true’ then theDESS will “hold” (i.e., standby and issue neither request).

To illustrate a bi-directional DESS embodiment, a simulation wasconducted for 1000 DESSs over a simulated timeframe of six days. Overthe course of six days, the system sees the ‘external’ variable loadillustrated in FIG. 10. Note that the load extends beyond 1.0 at itspeaks indicating that energy from the DESSs will be needed to meet theload. The load profile for days 1, 2, 5, and 6 are based on residentialdata. The load profile for days 3 and 4 are set artificially low toillustrate how excess supply can be used to bring the DESSs up to fullSOC. The base external supply is assumed to be constant at 60% of a 1.0load (see FIG. 11). FIG. 11 also shows net supply 1102 to the system ateach communication epoch from the DESSs. This does not match theexternal load exactly for there is additional load in charging DESSs(i.e., agents) with low SOC.

1000 DESS agents were utilized with control automatons configured with aminimum charge threshold, C_thresh, of 0.4, meaning they were configuredto ensure at least 0.4 SOC was maintained (see FIG. 12). In one example,C_thresh is an end-user defined parameter related to an end user'sdesired quality of service. Note that C_thresh could be arbitrarily setand does not need to be the same across all DESS agents. SOC for the1000 DESSs were randomly assigned (0 to 1) at the beginning of thesimulation.

At each communication epoch, a DESS agent charged (dark gray),discharged (medium gray), or held (light gray) as seen in FIG. 14. Thetotal buys/sells/holds are shown in FIG. 15 and their sum equals thenumber of agents (1000). More dynamics in the load (FIG. 10) leads tomore dynamics and disparity in the DESS agent's SOC (FIGS. 12 and 13).

Example of a Coordinator Operating with Both Homogeneous andHeterogeneous Loads.

The following describes and illustrates an example of a singlecoordinator controlling a diverse fleet of heterogeneous DERs.Specifically, the following case-study illustrates how 1500heterogeneous packetized DERS—TCL (1000), PEV (250), and ESS (250)—canall be coordinated under with single coordinator and simultaneouslytrack a reference signal (in the aggregate) and satisfy (local) QoSconstraints.

The uncontrollable background demand for each load type describes therandom perturbations to the local dynamic state. TCL: for the 1000residential electric water heaters, the uncontrollable demand representsthe use of hot-water in the home, such as a shower and running thewashing machine or dishwasher. For this numerical example, models weredeveloped based on statistics found in the literature for the energy usepatterns of electric water heaters. PEV: the background demand in thecase of the 250 plug-in electric vehicle batteries represent the drivingpatterns that discharge the battery. The PEV travel patterns wererandomly sampled from travel survey data for New England, which providesthe stochastic model for residential arrival and departure times, aswell as miles driven. From an assumed electric driving range of 150miles and an electric driving efficiency of 6.7 miles-per-kWh, the totalreduction in SOC is computed upon arriving home (to charge). ESS: the250 home batteries were based on specifications representative of alarge battery manufacturers residential energy storage units typical ofa large battery manufacturer, which each have a battery capacity of 13.5kWh, charge and discharge efficiency of around 95% (roundtrip of 92%),and a maximum (continuous) power rating of 5.0 kW. It was assumed thatthe battery owner stochastically charges or discharges the battery basedon a Gaussian random walk with a minimum power draw of 1.5 kW in eitherdirection. This could be representative of excess or deficit residentialsolar PV production or short-term islanding conditions.

The N=1500 diverse DER devices are then packetized and, over an 8-hourperiod (16:00 to 24:00), the coordinator will interact with the loadsand from 18:40 to 24:00 the coordinator tracks a mean-reverting randomsignal that represents a balancing signal from the ISO. The tracking isachieved by denying or accepting packet requests based on real-timeerror between reference and aggregated coordinator power output asdescribed earlier. The tracking errors are less than 5% for packetepochs of 6=5 minutes. FIG. 16 illustrates the tracking performance ofthe coordinator and that QOS requirements are satisfied as well.

Referring to FIG. 17, consider two coordinators: one is comprised of1000 TCLs, 250 PEVs, and 250 ESS batteries, a diverse coordinator 1702while the other contains 1500 TCLs—TCL-only coordinator 1504. FIG. 17illustrates how these two coordinators perform in tracking a referencesignal 1706 (which for ease of comparison would correspond to signalr(t) in FIGS. 6A-6C) composed of step, periodic, and ramp changes. FIG.17 shows the diverse coordinator 1702 outperforms the TCL-onlycoordinator 1704. More specifically, the root mean square tracking errorfor the diverse coordinator 1702 is four times smaller than the TCL-onlycoordinator 1594 (54 kW vs. 220 kW). Also, the gain in performance comeswithout sacrificing QoS as the TCLs in both coordinators 1702, 1704,experience nearly identical mean absolute deviation from the temperatureset-point: 2.4.degree. C. vs. 2.5.degree. C. (with similar standarddeviations). To further illustrate the value of a diverse fleet ofresources, FIGS. 18A and 18B provide the ON/OFF statuses for each devicein the respective coordinators. Careful comparison of the coordinatorillustrate that the TCL-only coordinator fails to track the lower partsof the reference signal due to many TCLs opting out (i.e., transitionsto exit-ON) as signified by very long continuous ON periods for theTCL-only coordinator in FIGS. 18A and 18B. That is, diversity indistributed energy resources not only improves tracking ability, butalso improves QoS delivered to end-consumer.

FIG. 19 illustrates an example method 1900 for requesting electricalpower during a communication epoch. The method 1900 includes determining103 a DER state as a first state, with a first request probability, or asecond state, with a second request probability. In another example,determining 103 may include selecting a charge request probabilityprofile. A charge request probability for the communication epoch isdetermined 106. The determined 106 charge request probabilitycorresponds to the determined 103 DER state (or in some examples, aprobability profile) and in some examples, may also be a function of aDER condition, e.g., a locally sensed condition (both as described aboveand further described below). A charge request may be sent 109 based onthe determined 106 charge request probability.

In some embodiments, the method 1900 may be performed on a DER that is aDESS. As such, the method 1900 may also include assessing a state ofcharge as the DER locally sensed condition. The method 1900 may furthercomprise determining 112 a discharge DER state as a first dischargestate, with a first discharge probability, or a second discharge state,with a second discharge probability. In another example, the determiningstep 112 may include determining or selecting a discharge requestprobability profile. A discharge request probability may be determined115 for a communication epoch, corresponding in one example to thedetermined 112 discharge DER state and the DER locally sensed conditionor in some examples, a probability determined from a selectedprobability profile and locally sensed condition (e.g., SOC). Adischarge request may be sent 118 based on the discharge requestprobability. In some embodiments, the charge request probabilityapproaches 1 as the state of charge decreases to a charge threshold,C_thresh, and the discharge request probability approaches 1 as thestate of charge increases to a discharge threshold, D_thresh, whereC_thresh<D_thresh. In some embodiments, no charge request or dischargerequest is sent if the request probability and discharge requestprobability would otherwise cause both a charge request and a dischargerequest to be sent. For example, steps 103-109 and steps 112-118 may beperformed in parallel and a decision made as to whether or not to makeeither a charge or discharge request or to stand-by as described herein.

Any one or more of the aspects and embodiments described herein may beconveniently implemented using one or more machines (e.g., one or morecomputing devices that are utilized as a user computing device for anelectronic document, one or more server devices, such as a documentserver, etc.) programmed according to the teachings of the presentspecification, as will be apparent to those of ordinary skill in thecomputer art. Appropriate software coding can readily be prepared byskilled programmers based on the teachings of the present disclosure, aswill be apparent to those of ordinary skill in the software art. Aspectsand implementations discussed above employing software and/or softwaremodules may also include appropriate hardware for assisting in theimplementation of the machine executable instructions of the softwareand/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 20 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 2000 withinwhich a set of instructions for causing a control system, such as theDER management system 308 of FIG. 3, to perform any one or more of theaspects and/or methodologies of the present disclosure may be executed.It is also contemplated that multiple computing devices may be utilizedto implement a specially configured set of instructions for causing oneor more of the devices to perform any one or more of the aspects and/ormethodologies of the present disclosure. Computer system 2000 includes aprocessor 2004 and a memory 2008 that communicate with each other, andwith other components, via a bus 2012. Bus 2012 may include any ofseveral types of bus structures including, but not limited to, a memorybus, a memory controller, a peripheral bus, a local bus, and anycombinations thereof, using any of a variety of bus architectures.

Memory 2008 may include various components (e.g., machine-readablemedia) including, but not limited to, a random access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 2016 (BIOS), including basic routines thathelp to transfer information between elements within computer system2000, such as during start-up, may be stored in memory 2008. Memory 2008may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 2020 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 2008 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 2000 may also include a storage device 2024. Examples ofa storage device (e.g., storage device 2024) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 2024 may beconnected to bus 2012 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device2024 (or one or more components thereof) may be removably interfacedwith computer system 2000 (e.g., via an external port connector (notshown)). Particularly, storage device 2024 and an associatedmachine-readable medium 2028 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 2000. In one example,software 2020 may reside, completely or partially, withinmachine-readable medium 2028. In another example, software 2020 mayreside, completely or partially, within processor 2004.

Computer system 2000 may also include an input device 2032. In oneexample, a user of computer system 2000 may enter commands and/or otherinformation into computer system 2000 via input device 2032. Examples ofan input device 2032 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 2032may be interfaced to bus 2012 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 2012, and any combinations thereof. Input device 2032may include a touch screen interface that may be a part of or separatefrom display 2036, discussed further below. Input device 2032 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 2000 via storage device 2024 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 2040. A networkinterface device, such as network interface device 2040, may be utilizedfor connecting computer system 2000 to one or more of a variety ofnetworks, such as network 2044, and one or more remote devices 2048connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 2044, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 2020, etc.) may be communicated to and/or fromcomputer system 2000 via network interface device 2040.

Computer system 2000 may further include a video display adapter 2052for communicating a displayable image to a display device, such asdisplay device 2036. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 2052 and display device 2036 maybe utilized in combination with processor 2004 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 2000 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 2012 via a peripheral interface 2056.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

Various modifications and additions can be made without departing fromthe spirit and scope of this invention. Features of each of the variousembodiments described above may be combined with features of otherdescribed embodiments as appropriate in order to provide a multiplicityof feature combinations in associated new embodiments. Furthermore,while the foregoing describes a number of separate embodiments, what hasbeen described herein is merely illustrative of the application of theprinciples of the present invention. Additionally, although particularmethods herein may be illustrated and/or described as being performed ina specific order, the ordering is highly variable within ordinary skillto achieve aspects of the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

The foregoing has been a detailed description of illustrativeembodiments of the invention. It is noted that in the presentspecification and claims appended hereto, conjunctive language such asis used in the phrases “at least one of X, Y and Z” and “one or more ofX, Y, and Z,” unless specifically stated or indicated otherwise, shallbe taken to mean that each item in the conjunctive list can be presentin any number exclusive of every other item in the list or in any numberin combination with any or all other item(s) in the conjunctive list,each of which may also be present in any number. Applying this generalrule, the conjunctive phrases in the foregoing examples in which theconjunctive list consists of X, Y, and Z shall each encompass: one ormore of X; one or more of Y; one or more of Z; one or more of X and oneor more of Y; one or more of Y and one or more of Z; one or more of Xand one or more of Z; and one or more of X, one or more of Y and one ormore of Z.

Various modifications and additions can be made without departing fromthe spirit and scope of this invention. Features of each of the variousembodiments described above may be combined with features of otherdescribed embodiments as appropriate in order to provide a multiplicityof feature combinations in associated new embodiments. Furthermore,while the foregoing describes a number of separate embodiments, what hasbeen described herein is merely illustrative of the application of theprinciples of the present invention. Additionally, although particularmethods herein may be illustrated and/or described as being performed ina specific order, the ordering is highly variable within ordinary skillto achieve aspects of the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

What is claimed is:
 1. A method of providing ancillary services to apower grid with a packetized energy management (PEM) coordinator, themethod comprising: receiving a reference signal from the power grid;filtering the reference signal to create a filtered reference signal;determining, according to the filtered reference signal, whether togrant or deny permission to a plurality of distributed energy resources(DERs) in communication with the PEM coordinator to draw packets ofpower from or discharge packets of power to the power grid; and grantingor denying permission to the DERs to draw or discharge power packets. 2.The method of claim 1, wherein the filtering includes applying at leastone of a low-pass filter to the reference signal so that a rate ofchange of the filtered reference signal is less than a rate of change ofthe reference signal or a high-pass filter to the reference signal sothat a rate of change of the filtered reference signal is greater than arate of change of the reference signal.
 3. The method of claim 1,wherein the filtering includes applying a low-pass filter after a powershortage period to limit a rate of increase in power packet consumptionby the DERs to prevent synchronization of the DERs.
 4. The method ofclaim 1, wherein the filtering includes receiving a directionalprediction of the reference signal and selecting a filtered referencesignal to optimize a tracking of the DER's net energy load to thedirectional prediction of the reference signal.
 5. The method of claim4, wherein the filtering further comprises: receiving or determining anestimate of an aggregate need for energy (NFE) of the DERs; anddetermining the filtered reference signal according to the NFE; whereinthe NFE is at least one of a measure of an aggregate amount of energycurrently stored by the DERs and a prediction of a future need forenergy by the DERs.
 6. The method of claim 1, wherein the filteringincludes applying a finite impulse response filter.
 7. The method ofclaim 1, wherein the DERs are configured to independently determine,according to a request probability profile, whether to request to drawpackets of power from or discharge packets of power to the power grid.8. The method of claim 1, further comprising randomizing a PEM controlparameter to minimize synchronization of the drawing or discharging ofpower packets by the DERs.
 9. The method of claim 8, wherein the PEMcontrol parameter is at least one of a control epoch length, acommunication epoch length, and a minimum off time between controlepochs, the method further comprising transmitting the randomized PEMcontrol parameter to the DERs.
 10. The method of claim 8, wherein thePEM control parameter is a PEM opt-in value for a locally-sensedcondition of the DER for opting-in to PEM control of the DER by thecoordinator after the locally-sensed condition exceeds a quality ofservice bound.
 11. A method of providing ancillary services to a powergrid with a packetized energy management (PEM) coordinator, the methodcomprising: determining, with a virtual battery model, an aggregate needfor energy (NFE) of a plurality of distributed energy resources (DERs)in communication with the coordinator; receiving, from a grid operator,a power grid reference signal; and determining, from the NFE and thepower grid reference signal, whether to grant or deny requests from theDERs to receive or discharge power packets.
 12. The method of claim 11,wherein the NFE is at least one of a measure of an aggregate amount ofenergy currently stored by the DERs and a prediction of a future needfor energy by the DERs.
 13. The method of claim 11, wherein the powergrid reference signal is at least one of an automatic generation controlsignal, a spinning reserves dispatch signal, or an inverse of wholesaleenergy prices.
 14. The method of claim 11, further comprising:receiving, from the grid operator, a directional prediction of the powergrid reference signal; determining, from the NFE and directionalprediction, a packetized energy management (PEM) control signal; andtransmitting the PEM control signal to the plurality of DERs.
 15. Themethod of claim 14, wherein the PEM control signal instructs theplurality of DERs to shift to a higher or lower request probabilityprofile, the request probability profile defining a probability acorresponding DER will request a power packet during a communicationepoch time interval.
 16. The method of claim 15, wherein the requestprobability profile defines a probability a DER will request a powerpacket during a communication epoch time interval as a function of alocally-sensed condition of the DER.
 17. The method of claim 16, whereinthe locally-sensed condition is a temperature or state of charge of theDER.
 18. The method of claim 14, wherein the PEM control signalinstructs DERs with a locally-sensed condition that exceeds a thresholdto transition to a standby mode and not request power packets.
 19. Themethod of claim 11, wherein the plurality of DERs are located in orelectrically proximate a plurality of geographic regions of the powergrid, wherein the determining the NFE includes determining an NFE foreach geographic region of DERs and the receiving includes receiving apower grid reference signal for each geographic region.
 20. A computingdevice, comprising: a processor configured to perform a packetizedenergy management (PEM) application, the PEM application includinginstructions for causing the processor to: receive a reference signalfrom a power grid; filter the reference signal to create a filteredreference signal; determine, according to the filtered reference signal,whether to grant or deny permission to a plurality of distributed energyresources (DERs) in communication with the computing device to drawpackets of power from or discharge packets of power to the power grid;and grant or deny permission to the DERs to draw or discharge powerpackets.
 21. The computing device of claim 20, wherein the filteringincludes applying at least one of a low-pass filter to the referencesignal so that a rate of change of the filtered reference signal is lessthan a rate of change of the reference signal or a high-pass filter tothe reference signal so that a rate of change of the filtered referencesignal is greater than a rate of change of the reference signal.
 22. Thecomputing device of claim 20, wherein the filtering includes applying alow-pass filter after a power shortage period to limit a rate ofincrease in power packet consumption by the DERs to preventsynchronization of the DERs.
 23. The computing device of claim 20,wherein the filtering includes receiving a directional prediction of thereference signal and selecting a filtered reference signal to optimize atracking of the DER's net energy load to the directional prediction ofthe reference signal.
 24. The computing device of claim 23, wherein thefiltering further comprises: receiving or determining an estimate of anaggregate need for energy (NFE) of the DERs; and determining thefiltered reference signal according to the NFE; wherein the NFE is atleast one of a measure of an aggregate amount of energy currently storedby the DERs and a prediction of a future need for energy by the DERs.25. The computing device of claim 20, wherein the filtering includesapplying a finite impulse response filter.
 26. The computing device ofclaim 20, wherein the DERs are configured to independently determine,according to a request probability profile, whether to request to drawpackets of power from or discharge packets of power to the power grid.27. A computing device, comprising: a processor configured to perform apacketized energy management (PEM) application, the PEM applicationincluding instructions for causing the processor to: determine, with avirtual battery model, an aggregate need for energy (NFE) of a pluralityof distributed energy resources (DERs) in communication with thecomputing device; receive, from a grid operator, a power grid referencesignal; and determine, from the NFE and the power grid reference signal,whether to grant or deny requests from the DERs to receive or dischargepower packets.
 28. The computing device of claim 27, wherein the NFE isat least one of a measure of an aggregate amount of energy currentlystored by the DERs and a prediction of a future need for energy by theDERs.
 29. The computing device of claim 27, wherein the power gridreference signal is at least one of an automatic generation controlsignal, a spinning reserves dispatch signal, or an inverse of wholesaleenergy prices.
 30. The computing device of claim 27, the PEM applicationfurther including instructions for causing the processor to: receive,from the grid operator, a directional prediction of the power gridreference signal; determine, from the NFE and directional prediction, apacketized energy management (PEM) control signal; and transmit the PEMcontrol signal to the plurality of DERs.
 31. The computing device ofclaim 30, wherein the PEM control signal instructs the plurality of DERsto shift to a higher or lower request probability profile, the requestprobability profile defining a probability a corresponding DER willrequest a power packet during a communication epoch time interval. 32.The computing device of claim 31, wherein the request probabilityprofile defines a probability a DER will request a power packet during acommunication epoch time interval as a function of a locally-sensedcondition of the DER.
 33. The computing device of claim 32, wherein thelocally-sensed condition is a temperature or state of charge of the DER.34. The computing device of claim 30, wherein the PEM control signalinstructs DERs with a locally-sensed condition that exceeds a thresholdto transition to a standby mode and not request power packets.
 35. Thecomputing device of claim 27, wherein the plurality of DERs are locatedin or electrically proximate a plurality of geographic regions of thepower grid, wherein the determining the NFE includes determining an NFEfor each geographic region of DERs and the receiving includes receivinga power grid reference signal for each geographic region.